def main():
    batch = get_batch()

    filename = 'saga_%s.hdf5' % str(batch)
    env = Environment(trajectory='Example_22_Euler_Integration_%s' % str(batch),
                      filename=filename,
                      file_title='Example_22_Euler_Integration',
                      comment='Go for Euler!',
                      overwrite_file=True,
                      multiproc=True,  # Yes we can use multiprocessing within each batch!
                      ncores=4)

    traj = env.trajectory
    trajectory_name = traj.v_name

    # 1st a) phase parameter addition
    add_parameters(traj)

    # 1st b) phase preparation
    # We will add the differential equation (well, its source code only) as a derived parameter
    traj.f_add_derived_parameter(FunctionParameter,'diff_eq', diff_lorenz,
                                 comment='Source code of our equation!')

    # explore the trajectory
    explore_batch(traj, batch)

    # 2nd phase let's run the experiment
    # We pass `euler_scheme` as our top-level simulation function and
    # the Lorenz equation 'diff_lorenz' as an additional argument
    env.run(euler_scheme, diff_lorenz)

    def test_maximum_overview_size(self):

        filename = make_temp_dir('maxisze.hdf5')

        env = Environment(trajectory='Testmigrate', filename=filename,

                          log_config=get_log_config())

        traj = env.v_trajectory
        for irun in range(pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH):
            traj.f_add_parameter('f%d.x' % irun, 5)

        traj.f_store()


        store = ptcompat.open_file(filename, mode='r+')
        table = ptcompat.get_child(store.root,traj.v_name).overview.parameters_overview
        self.assertEquals(table.nrows, pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH)
        store.close()

        for irun in range(pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH,
                  2*pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH):
            traj.f_add_parameter('f%d.x' % irun, 5)

        traj.f_store()

        store = ptcompat.open_file(filename, mode='r+')
        table = ptcompat.get_child(store.root,traj.v_name).overview.parameters_overview
        self.assertEquals(table.nrows, pypetconstants.HDF5_MAX_OVERVIEW_TABLE_LENGTH)
        store.close()

        env.f_disable_logging()

def main():

    filename = os.path.join('hdf5', 'Clustered_Network.hdf5')
    # If we pass a filename to the trajectory a new HDF5StorageService will
    # be automatically created
    traj = Trajectory(filename=filename,
                    dynamically_imported_classes=[BrianMonitorResult,
                                                  BrianParameter])

    # Let's create and fake environment to enable logging:
    env = Environment(traj, do_single_runs=False)


    # Load the trajectory, but onyl laod the skeleton of the results
    traj.f_load(index=-1, load_parameters=2, load_derived_parameters=2, load_results=1)

    # Find the result instances related to the fano factor
    fano_dict = traj.f_get_from_runs('mean_fano_factor', fast_access=False)

    # Load the data of the fano factor results
    ffs = fano_dict.values()
    traj.f_load_items(ffs)

    # Extract all values and R_ee values for each run
    ffs_values = [x.f_get() for x in ffs]
    Rees = traj.f_get('R_ee').f_get_range()

    # Plot average fano factor as a function of R_ee
    plt.plot(Rees, ffs_values)
    plt.xlabel('R_ee')
    plt.ylabel('Avg. Fano Factor')
    plt.show()

    # Finally disable logging and close all log-files
    env.disable_logging()

    def test_logging_stdout(self):
        filename = 'teststdoutlog.hdf5'
        filename = make_temp_dir(filename)
        folder = make_temp_dir('logs')
        env = Environment(trajectory=make_trajectory_name(self),
                          filename=filename, log_config=get_log_config(),
                          # log_levels=logging.CRITICAL, # needed for the test
                          log_stdout=('STDOUT', 50), #log_folder=folder
                          )

        env.f_run(log_error)
        traj = env.v_traj
        path = get_log_path(traj)

        mainstr = 'sTdOuTLoGGinG'
        print(mainstr)
        env.f_disable_logging()

        mainfilename = os.path.join(path, 'LOG.txt')
        with open(mainfilename, mode='r') as mainf:
            full_text = mainf.read()

        self.assertTrue(mainstr in full_text)
        self.assertTrue('4444444' not in full_text)
        self.assertTrue('DEBUG' not in full_text)

    def test_without_pre_run(self):
        runner = NetworkRunner()
        components = [TheNeurons(), TheConnection(), TheSimulation()]
        analyser = [TheMonitors()]
        nm = NetworkManager(network_runner=runner, component_list=components,
                            analyser_list=analyser)


        env = Environment(trajectory='Test_'+repr(time.time()).replace('.','_'),
                          filename=make_temp_dir(os.path.join(
                              'experiments',
                              'tests',
                              'briantests',
                              'HDF5',
                               'briantest.hdf5')),
                          file_title='test',
                          log_config=get_log_config(),
                          dynamic_imports=['pypet.brian2.parameter.BrianParameter',
                                                        BrianMonitorResult, BrianResult],
                          multiproc=True,
                          ncores=2)
        traj = env.v_traj
        nm.add_parameters(traj)
        traj.f_explore({'v01': [11*mV, 13*mV]})
        env.f_run(nm.run_network)
        self.check_data(traj)

def main():

    env = Environment(trajectory='FiringRate',
                      comment='Experiment to measure the firing rate '
                            'of a leaky integrate and fire neuron. '
                            'Exploring different input currents, '
                            'as well as refractory periods',
                      add_time=False, # We don't want to add the current time to the name,
                      log_folder='./logs/',
                      log_level=logging.INFO,
                      log_stdout=True,
                      multiproc=True,
                      ncores=2, #My laptop has 2 cores ;-)
                      wrap_mode='QUEUE',
                      filename='./hdf5/', # We only pass a folder here, so the name is chosen
                      # automatically to be the same as the Trajectory
                      )

    traj = env.v_trajectory

    # Add parameters
    add_parameters(traj)

    # Let's explore
    add_exploration(traj)

    # Ad the postprocessing function
    env.f_add_postprocessing(neuron_postproc)

    # Run the experiment
    env.f_run(run_neuron)

def main():
    """ Main *boilerplate* function to start simulation """
    # Now let's make use of logging
    logger = logging.getLogger()

    # Create folders for data and plots
    folder = os.path.join(os.getcwd(), 'experiments', 'ca_patterns_pypet')
    if not os.path.isdir(folder):
        os.makedirs(folder)
    filename = os.path.join(folder, 'all_patterns.hdf5')

    # Create an environment
    env = Environment(trajectory='cellular_automata',
                      multiproc=True,
                      ncores=4,
                      wrap_mode='QUEUE',
                      filename=filename,
                      overwrite_file=True)

    # extract the trajectory
    traj = env.traj

    traj.v_lazy_adding = True
    traj.par.ncells = 400, 'Number of cells'
    traj.par.steps = 250, 'Number of timesteps'
    traj.par.rule_number = 30, 'The ca rule'
    traj.par.initial_name = 'random', 'The type of initial state'
    traj.par.seed = 100042, 'RNG Seed'


    # Explore
    exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
                'initial_name' : ['single', 'random'],}
    # # You can uncomment the ``exp_dict`` below to see that changing the
    # # exploration scheme is now really easy:
    # exp_dict = {'rule_number' : [10, 30, 90, 110, 184],
    #             'ncells' : [100, 200, 300],
    #             'seed': [333444555, 123456]}
    exp_dict = cartesian_product(exp_dict)
    traj.f_explore(exp_dict)

    # Run the simulation
    logger.info('Starting Simulation')
    env.run(wrap_automaton)

    # Load all data
    traj.f_load(load_data=2)

    logger.info('Printing data')
    for idx, run_name in enumerate(traj.f_iter_runs()):
        # Plot all patterns
        filename = os.path.join(folder, make_filename(traj))
        plot_pattern(traj.crun.pattern, traj.rule_number, filename)
        progressbar(idx, len(traj), logger=logger)

    # Finally disable logging and close all log-files
    env.disable_logging()

def test_run():

    global filename


    np.random.seed()
    trajname = 'profiling'
    filename = make_temp_dir(os.path.join('hdf5', 'test%s.hdf5' % trajname))

    env = Environment(trajectory=trajname, filename=filename,
                      file_title=trajname,
                      log_stdout=False,
                      results_per_run=5,
                      derived_parameters_per_run=5,
                      multiproc=False,
                      ncores=1,
                      wrap_mode='LOCK',
                      use_pool=False,
                      overwrite_file=True)

    traj = env.v_trajectory

    traj.v_standard_parameter=Parameter

    ## Create some parameters
    param_dict={}
    create_param_dict(param_dict)
    ### Add some parameter:
    add_params(traj,param_dict)

    #remember the trajectory and the environment
    traj = traj
    env = env

    traj.f_add_parameter('TEST', 'test_run')
    ###Explore
    explore(traj)

    ### Make a test run
    simple_arg = -13
    simple_kwarg= 13.0
    env.f_run(simple_calculations,simple_arg,simple_kwarg=simple_kwarg)

    size=os.path.getsize(filename)
    size_in_mb = size/1000000.
    print('Size is %sMB' % str(size_in_mb))

    def test_parsing(self):

        filename = make_temp_dir('config_test.hdf5')
        env = Environment(filename=filename, config=self.parser)

        traj = env.v_traj
        self.assertTrue(traj.v_auto_load)
        self.assertEqual(traj.v_storage_service.filename, filename)

        self.assertEqual(traj.x, 42)
        self.assertEqual(traj.f_get('y').v_comment, 'This is the second variable')
        self.assertTrue(traj.testconfig)

        self.assertTrue(env._logging_manager.log_config is not None)
        self.assertTrue(env._logging_manager._sp_config is not None)

        env.f_disable_logging()

def main():
    """Main function to protect the *entry point* of the program.

    If you want to use multiprocessing under Windows you need to wrap your
    main code creating an environment into a function. Otherwise
    the newly started child processes will re-execute the code and throw
    errors (also see https://docs.python.org/2/library/multiprocessing.html#windows).

    """

    # Create an environment that handles running.
    # Let's enable multiprocessing with 2 workers.
    filename = os.path.join('hdf5', 'example_04.hdf5')
    env = Environment(trajectory='Example_04_MP',
                      filename=filename,
                      file_title='Example_04_MP',
                      log_stdout=True,
                      comment='Multiprocessing example!',
                      multiproc=True,
                      ncores=4,
                      use_pool=True,  # Our runs are inexpensive we can get rid of overhead
                      # by using a pool
                      freeze_input=True,  # We can avoid some
                      # overhead by freezing the input to the pool
                      wrap_mode=pypetconstants.WRAP_MODE_QUEUE,
                      graceful_exit=True,  # We want to exit in a data friendly way
                      # that safes all results after hitting CTRL+C, try it ;-)
                      overwrite_file=True)

    # Get the trajectory from the environment
    traj = env.trajectory

    # Add both parameters
    traj.f_add_parameter('x', 1.0, comment='I am the first dimension!')
    traj.f_add_parameter('y', 1.0, comment='I am the second dimension!')

    # Explore the parameters with a cartesian product, but we want to explore a bit more
    traj.f_explore(cartesian_product({'x':[float(x) for x in range(20)],
                                      'y':[float(y) for y in range(20)]}))

    # Run the simulation
    env.run(multiply)

    # Finally disable logging and close all log-files
    env.disable_logging()

def main():

    filename = os.path.join('hdf5', 'FiringRate.hdf5')
    env = Environment(trajectory='FiringRate',
                      comment='Experiment to measure the firing rate '
                            'of a leaky integrate and fire neuron. '
                            'Exploring different input currents, '
                            'as well as refractory periods',
                      add_time=False, # We don't want to add the current time to the name,
                      log_stdout=True,
                      log_config='DEFAULT',
                      multiproc=True,
                      ncores=2, #My laptop has 2 cores ;-)
                      wrap_mode='QUEUE',
                      filename=filename,
                      overwrite_file=True)

    traj = env.trajectory

    # Add parameters
    add_parameters(traj)

    # Let's explore
    add_exploration(traj)

    # Ad the postprocessing function
    env.add_postprocessing(neuron_postproc)

    # Run the experiment
    env.run(run_neuron)

    # Finally disable logging and close all log-files
    env.disable_logging()

def main():

    env = Environment(trajectory='FiringRatePipeline',
                      comment='Experiment to measure the firing rate '
                            'of a leaky integrate and fire neuron. '
                            'Exploring different input currents, '
                            'as well as refractory periods',
                      add_time=False, # We don't want to add the current time to the name,
                      log_folder='./logs/',
                      log_level=logging.INFO,
                      log_stdout=True,
                      multiproc=True,
                      ncores=2, #My laptop has 2 cores ;-)
                      filename='./hdf5/', # We only pass a folder here, so the name is chosen
                      # automatically to be the same as the Trajectory
                      )

    env.f_pipeline(mypipeline)

    def make_env(self, **kwargs):

        self.mode.__dict__.update(kwargs)
        filename = 'log_testing.hdf5'
        self.filename = make_temp_dir(filename)
        self.traj_name = make_trajectory_name(self)
        self.env = Environment(trajectory=self.traj_name,
                               filename=self.filename, **self.mode.__dict__)
        self.traj = self.env.v_traj

def main():
    filename = os.path.join('hdf5', 'FiringRate.hdf5')
    env = Environment(trajectory='FiringRatePipeline',
                      comment='Experiment to measure the firing rate '
                            'of a leaky integrate and fire neuron. '
                            'Exploring different input currents, '
                            'as well as refractory periods',
                      add_time=False, # We don't want to add the current time to the name,
                      log_stdout=True,
                      multiproc=True,
                      ncores=2, #My laptop has 2 cores ;-)
                      filename=filename,
                      overwrite_file=True)

    env.pipeline(mypipeline)

    # Finally disable logging and close all log-files
    env.disable_logging()

def main():
    """Main function to protect the *entry point* of the program.

    If you want to use multiprocessing with SCOOP you need to wrap your
    main code creating an environment into a function. Otherwise
    the newly started child processes will re-execute the code and throw
    errors (also see http://scoop.readthedocs.org/en/latest/usage.html#pitfalls).

    """

    # Create an environment that handles running.
    # Let's enable multiprocessing with scoop:
    filename = os.path.join('hdf5', 'example_21.hdf5')
    env = Environment(trajectory='Example_21_SCOOP',
                      filename=filename,
                      file_title='Example_21_SCOOP',
                      log_stdout=True,
                      comment='Multiprocessing example using SCOOP!',
                      multiproc=True,
                      freeze_input=True, # We want to save overhead and freeze input
                      use_scoop=True, # Yes we want SCOOP!
                      wrap_mode=pypetconstants.WRAP_MODE_LOCAL,  # SCOOP only works with 'LOCAL'
                      # or 'NETLOCK' wrapping
                      overwrite_file=True)

    # Get the trajectory from the environment
    traj = env.trajectory

    # Add both parameters
    traj.f_add_parameter('x', 1.0, comment='I am the first dimension!')
    traj.f_add_parameter('y', 1.0, comment='I am the second dimension!')

    # Explore the parameters with a cartesian product, but we want to explore a bit more
    traj.f_explore(cartesian_product({'x':[float(x) for x in range(20)],
                                      'y':[float(y) for y in range(20)]}))
    # Run the simulation
    env.run(multiply)

    # Let's check that all runs are completed!
    assert traj.f_is_completed()

    # Finally disable logging and close all log-files
    env.disable_logging()

 def test_net(self):
     env = Environment(trajectory='Test_'+repr(time.time()).replace('.','_'),
                       filename=make_temp_dir(os.path.join(
                           'experiments',
                           'tests',
                           'briantests',
                           'HDF5',
                            'briantest.hdf5')),
                       file_title='test',
                       log_config=get_log_config(),
                       dynamic_imports=['pypet.brian2.parameter.Brian2Parameter',
                                                     Brian2MonitorResult],
                       multiproc=False)
     traj = env.v_traj
     traj.f_add_parameter(Brian2Parameter, 'v0', 0.0*mV,
                          comment='Input bias')
     traj.f_explore({'v0': [11*mV, 13*mV, 15*mV]})
     env.f_run(run_network)
     self.get_data(traj)

def main():
    # Create an environment that handles running
    filename = os.path.join('hdf5','example_18.hdf5')
    env = Environment(trajectory='Multiplication',
                      filename=filename,
                      file_title='Example_18_Many_Runs',
                      overwrite_file=True,
                      comment='Contains many runs',
                      multiproc=True,
                      use_pool=True,
                      freeze_input=True,
                      ncores=2,
                      wrap_mode='QUEUE')

    # The environment has created a trajectory container for us
    traj = env.trajectory

    # Add both parameters
    traj.f_add_parameter('x', 1, comment='I am the first dimension!')
    traj.f_add_parameter('y', 1, comment='I am the second dimension!')

    # Explore the parameters with a cartesian product, yielding 2500 runs
    traj.f_explore(cartesian_product({'x': range(50), 'y': range(50)}))

    # Run the simulation
    env.run(multiply)

    # Disable logging
    env.disable_logging()

    # turn auto loading on, since results have not been loaded, yet
    traj.v_auto_load = True
    # Use the `v_idx` functionality
    traj.v_idx = 2042
    print('The result of run %d is: ' % traj.v_idx)
    # Now we can rely on the wildcards
    print(traj.res.crunset.crun.z)
    traj.v_idx = -1
    # Or we can use the shortcuts `rts_X` (run to set) and `r_X` to get particular results
    print('The result of run %d is: ' % 2044)
    print(traj.res.rts_2044.r_2044.z)

    def test_overwrite_annotations_and_results(self):

        filename = make_temp_dir('overwrite.hdf5')

        env = Environment(trajectory='testoverwrite', filename=filename,
                          log_config=get_log_config())

        traj = env.v_traj

        traj.f_add_parameter('grp.x', 5, comment='hi')
        traj.grp.v_comment='hi'
        traj.grp.v_annotations['a'] = 'b'

        traj.f_store()

        traj.f_remove_child('parameters', recursive=True)

        traj.f_load(load_data=2)

        self.assertTrue(traj.x == 5)
        self.assertTrue(traj.grp.v_comment == 'hi')
        self.assertTrue(traj.grp.v_annotations['a'] == 'b')

        traj.f_get('x').f_unlock()
        traj.grp.x = 22
        traj.f_get('x').v_comment='hu'
        traj.grp.v_annotations['a'] = 'c'
        traj.grp.v_comment = 'hu'

        traj.f_store_item(traj.f_get('x'), store_data=3)
        traj.f_store_item(traj.grp, store_data=3)

        traj.f_remove_child('parameters', recursive=True)

        traj.f_load(load_data=2)

        self.assertTrue(traj.x == 22)
        self.assertTrue(traj.grp.v_comment == 'hu')
        self.assertTrue(traj.grp.v_annotations['a'] == 'c')

        env.f_disable_logging()

    def test_throw_warning_if_old_kw_is_used(self):
        pass

        filename = make_temp_dir('hdfwarning.hdf5')

        with warnings.catch_warnings(record=True) as w:

            env = Environment(trajectory='test', filename=filename,
                              dynamically_imported_classes=[],
                              log_config=get_log_config())

        with warnings.catch_warnings(record=True) as w:
            traj = Trajectory(dynamically_imported_classes=[])

        traj = env.v_trajectory
        traj.f_store()

        with warnings.catch_warnings(record=True) as w:
            traj.f_load(dynamically_imported_classes=[])

        env.f_disable_logging()

def main():
    # Create an environment that handles running
    filename = os.path.join('hdf5', 'example_12.hdf5')
    env = Environment(trajectory='Multiplication',
                      filename=filename,
                      file_title='Example_12_Sharing_Data',
                      overwrite_file=True,
                      comment='The first example!',
                      continuable=False, # We have shared data in terms of a multiprocessing list,
                      # so we CANNOT use the continue feature.
                      multiproc=True,
                      ncores=2)

    # The environment has created a trajectory container for us
    traj = env.trajectory

    # Add both parameters
    traj.f_add_parameter('x', 1, comment='I am the first dimension!')
    traj.f_add_parameter('y', 1, comment='I am the second dimension!')

    # Explore the parameters with a cartesian product
    traj.f_explore(cartesian_product({'x':[1,2,3,4], 'y':[6,7,8]}))

    # We want a shared list where we can put all out results in. We use a manager for this:
    result_list = mp.Manager().list()
    # Let's make some space for potential results
    result_list[:] =[0 for _dummy in range(len(traj))]

    # Run the simulation
    env.run(multiply, result_list)

    # Now we want to store the final list as numpy array
    traj.f_add_result('z', np.array(result_list))

    # Finally let's print the result to see that it worked
    print(traj.z)

    #Disable logging and close all log-files
    env.disable_logging()